Innovation | LLM Optimizer Integration with Adobe Commerce

Why this exists

• Storefront brand guidelines are optimized for human shopper experiences such as visual layouts and click-through panels, which limits the depth and structure of product information available for LLM parsing.
• Brands with large catalogs, often exceeding 50K products, frequently maintain minimal or bare-bones product descriptions, forcing LLMs to infer product value primarily from configurations and attributes. Since LLMs reason through semantic relationships and contextual signals, the absence of rich descriptions and use-case phrases results in incomplete interpretation and effectively leaves product value to be guessed rather than explicitly communicated.

What changes

• Increase a brand’s product visibility across AI-powered discovery experiences by structuring, enriching, and governing commerce content so LLMs can confidently surface, recommend a brand’s products to match to a shopper intent.
• All enrichments done to a product(name, description, use case phrases) are delivered to the source (product catalog) ensuring consistent messaging across all sales channels.

What we don't do

How this works

Enrichment of Product Display Pages with all relevant information from the Commerce product catalog. This does not disrupt the human visible experience and influences only the hidden layer crawled by LLM bots. Enrichment of product name, description and introduction of use case phrases to ensure narrative value of the product is clear and understood by LLMs. The enrichment for this is done at the source (product catalog) which ensures consistent product messaging to all sales surfaces (Storefront, Advertisement pipelines, Marketplaces)

How we’ll measure success (please validate)

• Launch LLM Optimizer Integration for Commerce with the following milestones: • Beta announcement at Summit ‘26. • GA in 2H FY26